Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2305.06566.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T16:21:30.277477Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T09:27:41.409894Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation dabd4b83-6ef8-4b79-b600-e437dfeb6619 · inbound
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation cites this paper.
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:21:30.277477Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 749a5ec1-a755-4d5b-a401-a5d24a219cfc · inbound
Can Large Language Models Understand Preferences in Personalized Recommendation? cites this paper.
Can Large Language Models Understand Preferences in Personalized Recommendation? ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:17:33.641770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81818661-2fcc-46f8-ac3b-46ce02c12107 · inbound
Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors cites this paper.
Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T19:08:29.745670Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24ef984c-2774-43c0-a8eb-4c57c7d78374 · inbound
Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems cites this paper.
Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:06:56.812076Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41d3988b-7cfa-4ee6-920e-c4579b5e7853 · inbound
SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation cites this paper.
SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T09:27:41.411599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.